基于Faculty Opinions最新推荐评分的生物医学科技论文评价指标相关性研究

目的/意义分析不同生物医学科技论文数据库中的各类评价指标,挖掘其中隐含关联。方法/过程以Faculty Opinions推荐的文献为基础,分别从Altmetric、Scopus、WOS数据平台获取各类代表性指标,运用SPSS软件进行斯皮尔曼相关性比较分析。结果/结论不同学科类别文章Faculty Opinions最新推荐评分中,RS与WSS的相关度最高,RCR与RS相关度中等,Scopus各指标均与社会计量学各指标高度相关,Scopus与WOS各指标之间的相关度较高。

Opinion consensus incorporating higher-order interactions in individual-collective networks

In the current information society, the dissemination mechanisms and evolution laws of individual or collective opinions and their behaviors are the research hot topics in the field of opinion dynamics. First, in this paper, a two-layer network consisting of an individual-opinion layer and a collective-opinion layer is constructed, and a dissemination model of opinions incorporating higher-order interactions(i.e. OIHOI dissemination model) is proposed. Furthermore, the dynamic equations of opinion dissemination for both individuals and groups are presented. Using Lyapunov's first method,two equilibrium points, including the negative consensus point and positive consensus point, and the dynamic equations obtained for opinion dissemination, are analyzed theoretically. In addition, for individual opinions and collective opinions,some conditions for reaching negative consensus and positive consensus as well as the theoretical expression for the dissemination threshold are put forward. Numerical simulations are carried to verify the feasibility and effectiveness of the proposed theoretical results, as well as the influence of the intra-structure, inter-connections, and higher-order interactions on the dissemination and evolution of individual opinions. The main results are as follows.(i) When the intra-structure of the collective-opinion layer meets certain characteristics, then a negative or positive consensus is easier to reach for individuals.(ii) Both negative consensus and positive consensus perform best in mixed type of inter-connections in the two-layer network.(iii) Higher-order interactions can quickly eliminate differences in individual opinions, thereby enabling individuals to reach consensus faster.

Evolution and spatiotemporal analysis of earthquake public opinion based on social media data

As critical conduits for the dissemination of online public opinion,social media platforms offer a timely and effective means for managing emergencies during major disasters,such as earthquakes.This study focuses on the analysis of online public opinions following the Maduo M7.4 earthquake in Qinghai Province and the Yangbi M6.4 earthquake in Yunnan Province.By collecting,cleaning,and organizing post-earthquake Sina Weibo(short for Weibo)data,we employed the Latent Dirichlet Allocation(LDA)model to extract information pertinent to public opinion on these earthquakes.This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events.An emotion analysis,utilizing an emotion dictionary,categorized the emotional content of post-earthquake Weibo posts,facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes.The findings were visualized using Geographic Information System(GIS)techniques.The analysis revealed certain commonalities in online public opinion following both earthquakes.Notably,the peak of online engagement occurred within the first 24 hours post-earthquake,with a rapid decline observed between 24 to 48 hours thereafter.The variation in popularity of online public opinion was linked to aftershock occurrences.Adjusted for population factors,online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high.Initially dominated by feelings of“fear”and“surprise”,the public sentiment shifted towards a more positive outlook with the onset of rescue operations.However,distinctions in the online public response to each earthquake were also noted.Following the Yangbi earthquake,Yunnan Province reported the highest number of Weibo posts nationwide;in contrast,Qinghai Province ranked third post-Maduo earthquake,attributable to its smaller population size and extensive damage to communication infrastructure.This research offers a methodological approach for the analysis of online public opinion related to earthquakes,providing insights for the enhancement of post-disaster emergency management and public mental health support.

Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

Opinions of Students on the Contribution of the DIVA 3D Virtual Dissection Table to the Teaching of Anatomy at the Faculty of Medicine and Odontostomatology of Bamako

Objective: Study the contribution of the DIVA 3D dissection table in the teaching of anatomy at the Faculty of Medicine and Odontology of Bamako. Material and Methods: This was a qualitative study carried out from November 1 to December 30, 2023 at the clinical and morphological anatomy laboratory of the Faculty of Medicine and Odontostomatology of Bamako. Included in this study were students who participated in practical and tutorial sessions. The variables studied during this study were: the previous performance of dissection on a cadaver by the students, the opinion of the students on dissection on a cadaver, the replacement of dissection on a cadaver by virtual dissection in the absence of a body, the level student satisfaction. Results: We surveyed 130 participants. The average age was 22 ± 0.2 years with extremes of 17 and 29 years. 95.3% of participants were students. According to 66.7% of participants, virtual dissection is a good palliative in the absence of a corpse. 95.3% of participants found using the virtual dissection table easy with an average of 7.88 ± 1.4. The overall assessment was well rated by 99.3% of participants. Conclusion: According to the results of this study, the virtual dissection table should be improved by integrating commented videos. The use of the DIVA 3D virtual dissection table during practical and tutorial sessions is well appreciated by the students. We believe that the teaching of anatomy using 3D digital technology should be included in the study programs of the Faculty of Medicine and Odontostomatology.

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

Opinion of Stakeholders on the Management of Ecotourism in the Benue National Park of the North Region of Cameroon

Local communities living around protected areas stand high chance of benefiting from economic development and livelihood improvement through ecotourism if well planned. The objective of this study was to contribute to the understanding of stakeholders’ opinions on the valorization of ecotourism in order to propose alternative sustainable strategies. The study used a research method based on purposive sampling. Semi-structured questionnaires were administered to 125 household heads and 10 stakeholders were also interviewed with a question guide. The results were inputted into a student statistical package (SPSS) version 16. The results were analyzed using descriptive statistics and Chi-square was used to compare means. The results revealed that the test of dependence (Chi2) between the variables showed a significant difference at (p < 0.05). 51.9% of the respondent in ZIC 5 said that ecotourism has increased employment while respondents in the other sectors (ZIC 1, 4 and 9) who make up 76% of the respondents said ecotourism-related jobs in the park are seasonal and not frequent. Regarding the dependence of local communities on the park’s resources, 25% of ZIC 5 is highly dependent on the park, whereas, in the other sectors, only 18% are dependent on the park for their household income. This result is explained by the inaccessibility of local communities to the parks. The results of the impacts of ecotourism on the livelihoods of communities in localities reflect a general feeling of dissatisfaction with 88.8% of respondents manifesting their dissatisfaction with park authorities. Thus, 87.2% of respondents think that ecotourism does not bring significant economic benefits to the host communities. If ecotourism was planned, the benefits that accrue from activity could boost the economy of the area and support the conservation of the park, which so far remains insignificant. However, the strategy for enhancing ecotourism should be a participatory approach.

Kinetic Modeling of an Opinion Model on Social Networks

It is commonly accepted that, on social networks, the opinion of the agents with a higher connectivity, i.e., a larger number of followers, results in more convincing than that of the agents with a lower number of followers. By kinetic modeling approach, a kinetic model of opinion formation on social networks is derived, in which the distribution function depends on both the opinion and the connectivity of the agents. The opinion exchange process is governed by a Sznajd type model with three opinions, ±1, 0, and the social network is represented statistically with connectivity denoting the number of contacts of a given individual. The asymptotic mean opinion of a social network is determined in terms of the initial opinion and the connectivity of the agents.

Research on Prediction of Sentiment Trend of Food Safety Public Opinion

Emotion has a nearly decisive role in behavior, which will directly affect netizens’ views on food safety public opinion events, thereby affecting the development direction of public opinion on the event, and it is of great significance for food safety network public opinion to predict emotional trends to do a good job in food safety network public opinion guidance. In this paper, the dynamic text representation method XLNet is used to generate word vectors with context-dependent dependencies to distribute the text information of food safety network public opinion. Then, the word vector is input into the CNN-BiLSTM network for local semantic feature and context semantic extraction. The attention mechanism is introduced to give different weights according to the importance of features, and the emotional tendency analysis is carried out. Based on sentiment analysis, sentiment value time series data is obtained, and a time series model is constructed to predict sentiment trends. The sentiment analysis model proposed in this paper can well classify the sentiment of food safety network public opinion, and the time series model has a good effect on the prediction of food safety network public opinion sentiment trend. .

Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

Opinion Mining(OM)studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide.Though the interest in OM studies in the Arabic language is growing among researchers,it needs a vast number of investigations due to the unique morphological principles of the language.Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features.The comparative OM studies in the English language are wide and novel.But,comparative OM studies in the Arabic language are yet to be established and are still in a nascent stage.The unique features of the Arabic language make it essential to expand the studies regarding the Arabic text.It contains unique featuressuchasdiacritics,elongation,inflectionandwordlength.Thecurrent study proposes a Political Optimizer with Probabilistic Neural Network-based Comparative Opinion Mining(POPNN-COM)model for the Arabic text.The proposed POPNN-COM model aims to recognize comparative and non-comparative texts in Arabic in the context of social media.Initially,the POPNN-COM model involves different levels of data pre-processing to transform the input data into a useful format.Then,the pre-processed data is fed into the PNN model for classification and recognition of the data under different class labels.At last,the PO algorithm is employed for fine-tuning the parameters involved in this model to achieve enhanced results.The proposed POPNN-COM model was experimentally validated using two standard datasets,and the outcomes established the promising performance of the proposed POPNN-COM method over other recent approaches.

A Knowledge-Integrate Cross-Domain Data Generation Method for Aspect and Opinion Co-Extraction

To address the difficulty of training high-quality models in some specific domains due to the lack of fine-grained annotation resources, we propose in this paper a knowledge-integrated cross-domain data generation method for unsupervised domain adaptation tasks. Specifically, we extract domain features, lexical and syntactic knowledge from source-domain and target-domain data, and use a masking model with an extended masking strategy and a re-masking strategy to obtain domain-specific data that remove domain-specific features. Finally, we improve the sequence generation model BART and use it to generate high-quality target domain data for the task of aspect and opinion co-extraction from the target domain. Experiments were performed on three conventional English datasets from different domains, and our method generates more accurate and diverse target domain data with the best results compared to previous methods.

角色演变视角下辟谣信息对社交媒体意见领袖形成的影响——基于舆论领导法则

[目的/意义]探究辟谣信息对社交媒体用户的影响,特别是对意见领袖形成现象机制的挖掘。构建社交媒体用户角色演变行为模型,研究人员可以更好地了解意见领袖的形成原因,刻画辟谣信息对社交媒体用户的作用机制,有助于对参与辟谣的“准意见领袖”进行合理引导,以提高社交媒体舆情管理的效率和准确性。[方法/过程]首先,对微博社交媒体平台中的特定主题事件进行跟踪,识别并筛选出“新生意见领袖”群体,并利用质性访谈对此群体进行深度访谈。然后,基于程序化扎根理论方法,利用NVivo 12软件进行扎根编码构建模型,并通过理论饱和度检验验证模型的完整性。最后,对社交媒体用户角色演变的原因进行深入分析和探讨。[结果/结论]在辟谣信息作用下,个体主观因素、社会环境因素和辟谣信息因素对一般用户向新生意见领袖的演变行为产生重要影响,其中个体主观因素在辟谣信息的作用下对意见领袖形成的影响更加显著,自我认知清晰、高情绪需求且情绪稳定的个体更容易成为影响力持久的意见领袖;社会公众认可、实质性奖励和政策法律支持共同构成社会环境因素,是意见领袖形成的重要保障;辟谣信息因素中的辟谣信息热度、可信度和发布质量等方面都会直接影响“潜在意见领袖”用户群体对辟谣事件的把握,进而干预其参与辟谣的决定。

支持或反对:社交媒体用户观点的形成机理

探究社交媒体用户观点形成机理,揭示影响网民观点形成的重要因素,可深入理解网络用户的行为模式,洞察网络舆论本质。文章以“双减”话题微博数据为数据源,基于主题模型和机器学习的情感分析方法挖掘用户观点;从意见氛围、社交关系、前期行为特征和用户属性等维度构造特征变量,基于LightGBM建立不同主题下的情感预测模型,采用SHapley Additive exPlanations(SHAP)对特征重要性进行排序,发现影响网民观点形成的重要因素。研究发现:前期行为特征对用户观点形成的影响最大,意见氛围次之,社交关系即好友关系影响较弱;个体在情感倾向上更易跟随规模较大的传播主体,而不是崇拜权威。

“再塑造”与“高风险”:生成式人工智能对舆论安全的影响

[研究目的]探讨对信息内容具有强理解、强生成能力的生成式人工智能,将如何改变既有舆论生产流程,如何推动舆论传播发展,以及对舆论安全带来的影响。[研究方法]该研究结合既有研究,系统性分析生成式人工智能对舆论生产与传播的影响,揭示其可能对舆论安全造成的多方位冲击,并在此基础上提出完善舆论安全的举措。[研究结论]生成式人工智能将简化舆论生产流程,加速舆论格局转变,强化资本对舆论的影响力。基于该技术开展的意识形态侵蚀行为更加难以防御,对该技术的大规模使用将让舆论生态更混乱,舆论治理更困难,虚假信息生产与传播更加隐蔽,并外溢出难以预估的危险。为此需构建多方参与的舆论生产与传播监管格局,强化舆论引导能力,传递优质价值观。

新闻舆论在网络暴力治理中的功能思考——从沃尔特·李普曼的《Public Opinion》谈起

网络暴力通常以言论暴力、舆论极化的形式出现。网络暴力破坏了网络生态,并引起各国的普遍重视。由于新闻媒体议程及其报道框架影响着舆论的形成,新闻舆论对网络舆论的引导也就十分重要。当代人的“媒介化生存”产生了宏大的“拟态环境”,新闻议程对公众议程的塑造日益深重,为权威信源、新闻真相等介入网络舆论治理提供了可能性。主流媒体在场,可以消弭网络空间暴戾之气;权威新闻舆论,可以引导网络空间情感宣泄;发掘新闻真相,能够疏导网络舆论。同时,网络暴力的治理尚需多元力量的参与。

The Protection of Right of Minors to Express Their Opinions in Family Litigations

The right of minors to express their opinions in family litigations refers to minors,who have a certain degree of mental capacity and assertiveness,having the right to express their opinions on the arrangements or decisions related to themselves when the courts handle familial litigation cases involving their interests,and to request the courts hear their opinions.In essence,the right of minors to express their opinions in family litigations belongs to the basic procedural rights of minors as well as judicial beneficiary rights.As for the protection of the right of minors to express their opinions in family litigations,recognizing the status of minors as independent subjects of rights is the logical premise,the procedural guarantee for the rights of the substantive parties involved is the direct cause,and ensuring the best interests of children is the fundamental reason.In order to protect the right of minors to express their opinions in family litigations,China should standardize the mechanisms for judges to hear the opinions of minors directly,improve the mechanisms for family investigators,and establish mechanisms for representing the interests of minors.

基于改进SEIQR模型的突发网络舆情传播与控制研究

[目的/意义]社会突发事件映射到互联网空间中形成的突发网络舆情在不良因素的干扰下于网民间扩散容易形成强烈的负面情绪导向,很可能对网络社会的和谐稳定产生极大破坏。因此,正确认识突发网络舆情的传播特点,把握各种舆情管控措施对传播的影响,对合理、有效地管理舆情至关重要。[方法/过程]针对突发网络舆情的传播特性对标准SEIQR模型进行改进,提出实施舆情管控措施下以媒体恶意炒作和网民间恶性正反馈为主要传播驱动的SEACBR模型,并利用仿真实验对不同规模的突发网络舆情在各种管控措施下的传播过程进行模拟,以探究不同措施的管控效果并验证模型的泛化性。[结果/结论]管控突发网络舆情应以良性引导为主,其他措施为辅,这样在及时施策、积极治理的情况下突发网络舆情的消极影响就可被有效抑制。

美国涉疆舆论战:政治媒介化与舆论外交

2018年以来,美国针对中国新疆发起一波波舆论战,罗织的罪名令人匪夷所思。文本以政治媒介化理论观照美国涉疆舆论战。政治媒介化指在美西方市场逻辑作用下,政治逐渐依赖、内化媒介逻辑,政治过程受媒介逻辑长期形塑的现象。美国涉疆舆论战通过政治媒介化策略,将其遏制中国的目的隐蔽地转换为一个个“悲情故事”,妄图实现其舆论外交目的。文本深入剖析了美国涉疆舆论战中的政治媒介化过程与策略,揭露了美国涉疆舆论战的真相与本质。

Supervision of Public Opinion under the Background of Social Co-governance-Take the Changsheng Vaccine Incident as an Example

Objective To provide reference for the news media to give play to the role of public opinion supervision in time based on the background of drug safety and social co-governance.Methods The method of case analysis was used to make a retrospective study on the Changsheng vaccine incident in 2018.Then the role of mainstream media,pharmaceutical media,and self-media in the supervision of public opinion was investigated.Results and Conclusion Both mainstream and pharmaceutical media played an excellent role in supervising the Changchun Changsheng vaccine incident.However,the content published by some pharmaceutical media was hard to understand by ordinary people.Besides,the role of self-media in public opinion supervision was polarized.Some self-media closely kept pace with mainstream media in public opinion supervision.Other self-media unilaterally pursued the click rate,publishing false information to guide wrong public opinion.The news media should optimize the supervision efficiency of drug safety.On the one hand,pharmaceutical media should pay attention to the fact that readers may not understand the difficult terms because they are not professional.On the other hand,self-media practitioners should improve their professional quality so that they will not publish some fake news to mislead public opinion.

基于情感分析的网络舆情共振研究

为有效应对复杂多变的舆情环境,研究孤立发生的单一舆情事件演化成具有某些相同特征的多舆情事件簇或事件集,针对同议题、同情绪等多起舆情事件建立网络舆情共振模型,通过爬取“唐山打人案”和“唐山打人事件被害人首次发声”事件相关微博数据,以及“2021年河南遭遇特大暴雨”和“2023年河北暴雨”相关微博数据,将评论数据进行BosonNLP情感分析,得出其情感分数;并将情感分数作为模型参数,分别对2起不同类型的案例进行检验。研究结果表明:在原生舆情与次生舆情共同作用下,引起网民情绪感染和矛盾冲突,从而发生网络舆情共振,并且共振产生的热度高于单一事件热度;网民消极的态度值会加速舆情共振、不同类型的事件所产生的舆情共振效果是不同的。研究结果可丰富网络舆情以及社会物理学相关理论,可为构建舆情共振的研究框架提供参考。